Author's Accepted Manuscript
Neural activation during risky decisionmaking in youth at High risk for substance use Disorders Leslie A. Hulvershorn, Tom A. Hummer, Rena Fukunaga, Ellen Leibenluft, Peter Finn, Melissa A. Cyders, Amit Anand, Lauren Overhage, Allyson Dir, Joshua Brown www.elsevier.com/locate/psychresns
PII: DOI: Reference:
S0925-4927(15)00114-6 http://dx.doi.org/10.1016/j.pscychresns.2015.05.007 PSYN10368
To appear in:
Psychiatry Research: Neuroimaging
Received date: 7 July 2014 Revised date: 13 March 2015 Accepted date: 13 May 2015 Cite this article as: Leslie A. Hulvershorn, Tom A. Hummer, Rena Fukunaga, Ellen Leibenluft, Peter Finn, Melissa A. Cyders, Amit Anand, Lauren Overhage, Allyson Dir, Joshua Brown, Neural activation during risky decision-making in youth at High risk for substance use Disorders, Psychiatry Research: Neuroimaging, http://dx.doi.org/10.1016/j.pscychresns.2015.05.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Neural Activation During Risky Decision-Making in Youth at High Risk for Substance Use Disorders Leslie A. Hulvershorna*, Tom A. Hummera, Rena Fukunagab, Ellen Leibenluftc, Peter Finnb, Melissa A. Cydersd, Amit Anande, Lauren Overhagea, Allyson Dird, Joshua Brownb a
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
b
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN,
USA c
Section on Bipolar Spectrum Disorders, Intramural Research Program, NIMH,
Bethesda, MD, USA d
Department of Psychology, Indiana University Purdue University–Indianapolis,
Indianapolis, IN, USA e
Center for Behavioral Health, Cleveland Clinic, Cleveland, OH, USA
*Corresponding Author: Leslie A. Hulvershorn, MD 705 Riley Hospital Drive, Rm 4300 Indianapolis, IN 46205 Email: [email protected] Phone: 317-944-2008 Fax: 317-948-0609
Abstract Risky decision-making, particularly in the context of reward-seeking behavior, is strongly associated with the presence of substance use disorders (SUDs). However, there has been little research on the neural substrates underlying reward-related decision-making in drug-naïve youth who are at elevated risk for SUDs. Participants comprised 23 highrisk (HR) youth with a well-established SUD risk phenotype and 27 low-risk healthy comparison (HC) youth, aged 10-14. Participants completed the balloon analog risk task (BART), a task designed to examine risky decision-making, during functional magnetic resonance imaging. The HR group had faster reaction times, but otherwise showed no behavioral differences from the HC group. HR youth experienced greater activation when processing outcome, as the chances of balloon explosion increased, relative to HC youth, in ventromedial prefrontal cortex (vmPFC). As explosion probability increased, group-by-condition interactions in the ventral striatum/anterior cingulate and the anterior insula showed increasing activation in HR youth, specifically on trials when explosions occurred. Thus, atypical activation increased with increasing risk of negative outcome (i.e., balloon explosion) in a cortico-striatal network in the HR group. These findings identify candidate neurobiological markers of addiction risk in youth at high familial and phenotypic risk for SUDs. Keywords: Adolescent, Decision-making, risk, Addiction risk, Functional imaging, Prefrontal cortex
1. Introduction Decision-making refers to the process of “forming preferences, selecting and executing actions, and evaluating outcomes” (Ernst and Paulus, 2005). Theorists have identified a series of processes that occur during the choice phase of decision-making: initiation, monitoring, and completion of choice-related actions (Reyna and Rivers, 2008). The outcome phase follows, in which individuals learn and process the actual outcomes of their choices (Reyna and Rivers, 2008). Altered decision-making patterns have been observed in individuals with substance use disorders (SUDs), including preference for short-term gains (Grant et al., 2000; Bechara and Damasio, 2002) and riskier options (Lane and Cherek, 2000) and difficulty valuing the probability and magnitude of potential outcomes (Rogers and Robbins, 2001; Paulus et al., 2002; Paulus et al., 2003). Whether these decision-making deficits and their underlying neural substrates are the result of repeated use of drugs of abuse, predate SUDs, or both, remains unclear. Deficits in making choices have been hypothesized to originate from preexisting neurobiological abnormalities (Ernst and Paulus, 2005), while deficits in processing outcomes have been hypothesized to be more likely a consequence of substance use (Redish, 2004). To address this hypothesized distinction, the neural basis of decision-making must be better characterized in drug naïve individuals, with the eventual goal of longitudinally assessing neural activity in candidate regions as SUDs develop, as has been done with other imaging modalities (Norman et al., 2011). Because fewer than 15% of adolescents develop lifetime SUDs (Huang et al., 2006), targeting youth at high familial and phenotypic risk for SUDs might illuminate underlying neural mechanisms influencing the development of SUDs. Given that the mean onset of SUDs is age 14 (Swendsen et al., 2012), assessing decision-making in
high-risk preadolescent youth is warranted. Multiple addiction risk models have converged on the finding that youth with externalizing disorders [e.g., attentiondeficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD)], particularly those with a family history of addiction, are at elevated risk for the development of SUDs (Tarter et al., 2003; Zucker, 2008; Iacono et al., 2008; King et al., 2009). For example, among 10-12 year old boys followed to age 19, the risk model implemented here predicted SUDs with 85% accuracy and accounted for 50% of the variance in drug use (Tarter et al., 2003). Thus, we attempt to maximize risk for SUD development according to these models by recruiting a high-risk sample with both SUD family history and childhood externalizing psychopathology. While this complex risk phenotype does not allow for the dissociation of neural effects of externalizing psychopathology from those related to a family history of addiction, its high predictive power for SUD development is clinically significant. Findings revealed with this high-risk sample (versus healthy comparisons) warrant future investigations designed to disentangle the impact of externalizing disorders and familial factors. Most of the risky decision-making literature in externalizing disorders has focused on either adults (Miranda et al., 2009; Matthies et al., 2012; Duarte et al., 2012; Galvan et al., 2013) or youth behavioral outcomes (Drechsler et al., 2008; Fairchild et al., 2009; Drechsler et al., 2010; Schutter et al., 2011). These studies highlight an increased probability of disadvantageous decisions, correlations of risky-decision-making with impaired working memory and a propensity for lower probability/high reward choices in both youth and adults with externalizing psychopathology. Only one study appears to have directly examined the neural basis for risky-decision-making in youth with externalizing disorders (Crowley et al., 2010) and it focused on brain response during the outcome phase. Contrasting cautious low-yield with risky high-yield responses (n=20
adolescents with conduct/SUDs, in remission; n=20 adolescent controls), authors reported decreased activity during reward (anterior cingulate, temporal cortex and cerebellum) and increased activation during loss (orbitofrontal cortex, brain stem and cerebellum) in the conduct/SUD group. Thus, youth with externalizing disorders reliably demonstrate behavioral differences in risky-decision-making, but few studies have addressed the neural underpinnings of these differences. Several studies have examined neural activation associated with decisionmaking in youth identified as being at elevated risk for SUDs (Ivanov et al., 2012; Nees et al., 2012; Xiao et al., 2013). In these studies, youth deemed high risk for SUD, either by early/problem use or by family history have been shown to have activation abnormalities in cortical (OFC, insula), limbic and striatal circuits, although findings are inconsistent across studies, potentially due to heterogeneity in psychopathology and substance use. Most work has been conducted in older non-drug naïve youths, which likely confounds neural risk factors with early effects of substance use. Additionally, in this population, no studies have dissociated choice from responses to choice outcomes. Using the balloon analog risk task (BART), we examined the neural basis of choice and outcome phases of decision-making separately and characterized activation changes as both risk and reward increase across trials. The BART uses financial incentives to model real-world drug and alcohol choices by presenting participants with a series of risky-decisions and has been associated with psychopathy and impulsivity (Hunt et al., 2005), adolescent risk-taking (Lejuez et al., 2003b; Lejuez et al., 2007) and SUDs (Lejuez et al., 2003a; Hopko et al., 2006). In the sample most relevant to the present, youth with externalizing disorders (mean age 16) were found to have behavioral differences, specifically more inflations and more popped balloons, compared to healthy controls (Crowley et al., 2006). The initial version of the BART used in imaging (Rao et al., 2008) was modified from the original (Lejuez et al., 2002) to be able to dissociate
choice effects (i.e., choose to inflate or stop) from outcome effects (i.e., successful inflation, burst, and cash out). In healthy adults, fMRI BART studies have revealed that the choice phase of risky-decision making is linked to activation in a meso-limbic frontal network including midbrain, ventral and dorsal striatum, anterior insula, dorsal lateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC) (Rao et al., 2008; Fukunaga et al., 2012) and ventromedial prefrontal cortex (vmPFC) (Fukunaga et al., 2012; Schonberg et al., 2012). Findings associated with the outcome phase have been associated with a similar network involving insula, striatal, cerebellar and medial prefrontal regions (Galvan et al., 2013); Surprising outcomes have been associated with the medial PFC (Jessup et al., 2010). Increased responses to balloon explosions in lateral prefrontal cortex, insula, ACC and middle temporal gyrus was reported in adults with alcohol use disorders, relative to controls (Claus and Hutchison, 2012). Medial prefrontal activation was also correlated with adult alcohol use during balloon outcomes (Bogg et al., 2012). The BART has been studied in adolescents (Lejuez et al., 2003b; Lejuez et al., 2007), although only minimally during fMRI and not in the context of addiction risk (Chiu et al., 2012; Telzer et al., 2013b, a). We administered the BART to drug-naïve youth selected for high SUD risk (high risk; HR) and healthy comparisons (HC) during fMRI. The version of the BART used here (Bogg et al., 2012; Fukunaga et al., 2012) included a parametric modulation analysis that allowed for the study of a central question: How neural activation changes as risk for explosion changes. Since deficits in choice selection may predate drug involvement (Ernst and Paulus, 2005; Paulus et al., 2005), we hypothesize that HR youth will demonstrate insensitivity to increasing explosion probability in choice-relevant regions (e.g., anterior cingulate cortex (ACC), inferior frontal gyrus). We also hypothesize that neural response to choice outcomes will be more marked in HR youth in the vmPFC, ACC (Smith et al., 2010; Bogg et al., 2012) and
dorsal striatum, given its role in action-reward associations in humans (Balleine et al., 2007) and in prior BART studies (Rao et al., 2008). 2. Methods 2.1. Participants As detailed previously (Hulvershorn et al., 2013), we recruited right-handed, English-speaking 10-14 year-olds with at least one parent capable of reading and speaking English. To maximize familial risk for SUD development, HR participants were required to be biological offspring of men with past or present SUDs and to have an additional first- or second-degree family member with SUD history. Each HR participant also met DSM-IV-TR criteria for ADHD plus a disruptive behavior disorder [CD, ODD or disruptive behavior disorder, not otherwise specified (DBD NOS)]. More than five lifetime uses of drugs of abuse (including nicotine) or alcohol were exclusionary. HR participants were recruited largely from the community (radio, print and online ads), although a minority of youth were recruited directly from a psychiatric clinic (signs in clinic, notification by intake coordinator). HC participants had no current or lifetime history of any DSM-IV psychiatric diagnosis or SUDs (exceptions: specific phobias, enuresis, encopresis, learning disorders) and no first-degree relative with a history or current diagnosis of a SUD. HC participants were recruited in response to community postings. All individuals with in utero exposure to drugs or alcohol, per caregiver report, were excluded. Additional exclusion criteria for both groups included psychotic symptoms, pervasive developmental disorders, current depression or mania, or SUDs; psychopharmacologic treatment within the past 2 weeks other than psychostimulants (withheld the days of assessment and scanning, as is routine for pediatric ADHD neuroimaging studies, given little concern for withdrawal symptoms); history of
neurological problems; estimated Full-Scale IQ 5000 ms) was compared between groups. Balloons were also compared between groups for the following measures: balloons won, balloons exploded, inflations per balloon, minimum/maximum number of inflations and reaction times. Analysis of variance (ANOVA) was performed to examine group (HR vs. HC) by condition (choose win vs. choose inflate) interactions as well as main effects of group. To assess whether behavioral traits correlated with BART performance, UPPS-PC subscale scores were correlated with the following variables (listed in Table 4) using Pearson’s Correlations (averaged per participant): number of inflations per balloon, total number of inflations, total number of exploded, stopped and completed balloons and total average winnings.
2.6. Image analysis 2.6.1. Subject-level analyses. Image preprocessing, using AFNI software (Cox, 1996), consisted of slice-time correction, de-spiking of time series outliers (3dDespike algorithm), motion correction via realignment to the first time point using Fourier interpolation, registering the functional image to the structural image, correction for signal inhomogeneity with field mapping and spatial smoothing with a Gaussian kernel of 6-mm full-width at half-maximum. A general linear regression model (GLM) with random effects was created to estimate event-related responses. Along with six motion parameters and linear and quadratic detrending terms to correct for potential scanner drift, nine regressors that encompassed all potential decisions and outcomes, including parametric modulators when appropriate, and one additional nuisance regressor (reaction time outliers) were generated. Time points with >3.5-mm head displacement were censored (plus the time point before and two after). Participants with >10 time points censored were excluded. Choice events, aligned to the one repetition time (TR) that included the button press response, were modeled as Choose Inflate (choosing to continue inflating the balloon) or Choose Win (choosing to discontinue inflating and bank money) regressors. Outcome events were modeled as the TR that included balloon explosion (Outcome Explode), successful balloon inflation (Outcome Inflate), or the outcome of choosing to discontinue inflations (Outcome Win). Because there were relatively few Outcome Explode events, outliers (1.5*interquartile range) among those participants with fewer than five balloon explosions were excluded from Outcome contrasts (n=3 participants). For each subject, Choose Inflate and Choose Win conditions were contrasted. For outcome trials, only Outcome Inflate vs. Outcome Explode were contrasted, as these constitute the outcomes (positive or negative) that could follow an identical earlier
decision (Choose Inflate). We did not contrast Outcome Win (vs. either Outcome Inflate or Explode) because there was no uncertainty at this point in the trial (i.e., the subject had already banked the reward). Balloon explosion probabilities (Supplementary Table 1) were included as parametric modulators for each event-type regressor (e.g., Choose Inflate * P(explode), Outcome Inflate * P(explode)), except for Outcome Win, because the probability of explosion no longer applied at this point. The parametric modulator for a given inflation represented the explosion probability at each pump, not the total explosion probability that occurs until the balloon either explodes or is cashed outInclusion of parametric modulators accounts, in part, for the psychological impact of the changing balloon explosion probability across trials. Trials with reaction time outliers (>5000 ms) were modeled separately, but identically to all other balloons, and treated as nuisance regressors. Activation maps were warped to a standard Talairach atlas for group analyses. For subjects where P(explode) was the same for each explosion (2 HR, 1 HC), modulatory effects could not be separated from effects of the explosion itself, so these participants were excluded from outcome contrasts. 2.6.2. Group level analyses. Contrast maps for choice (Choose Inflate vs. Choose Win) and outcome (Outcome Inflate vs. Outcome Explode) were obtained separately from subject-level coefficients. To test for between-group differences on the contrasts, we carried out condition (e.g., Choose Inflate vs. Choose Win) by group within-subjects ANOVAs (using 3dMVM in AFNI) on activation intensities derived from the GLM (“Analysis of BOLD signal, without Parametric Modulation”). In addition, choice and outcome ANOVAs with parametric modulators (P(explode)) were also conducted
(“Parametric Modulator Analysis”). Activation intensities from each subject were extracted from significant clusters. Separately, to examine the potential influence of IQ or socioeconomic status (SES) on the findings, activation intensities from significant clusters were tested with analysis of covariance (ANCOVA) using SES (defined using family income on a 1-5 scale, where 1 = $80,000; Table 1) and full-scale IQ as covariates. Only clusters which remained significant (p < 0.05) after accounting for covariates are reported as primary findings. Incidentally, all clusters remained significant after accounting for covariates. Multiple comparisons associated with this whole-brain voxel-wise analysis were addressed using cluster-wise thresholds. Individual voxels were considered significant at p < 0.01, and a Monte Carlo simulation (AlphaSim) was again used to determine that cluster size (k) > 216 voxels corrected for group-level significance (p < 0.05). 3. Results 3.1. Participants Fifty-six right-handed male and female participants aged 10-14 years old completed the protocol. Six participants were excluded from all analyses for the following reasons: (1) two HR participants had >10 motion-censored time points; (2) two HR participants had high global signal variance across the time series, likely non-neural artifact; and (3) two HC participants had >10 trials with reaction times exceeding 5000 ms. Groups did not differ on motion in the scanner (Table 1). Groups were matched on age, gender, and Tanner stage, but they differed on IQ and SES (Table 1). Psychotropic medication treatment histories are presented in Table
1. See Table 2 for clinical characteristics of the child participants and Table 3 for paternal SUDs. 3.2. BART performance Groups did not differ on task outcomes, except for reaction times (Table 4), where HR youth were faster on win trials (F(1, 46)=7.27, p=0.01). Twelve participants (11 HC; 1 HR) required modeling with the nuisance regressor for outlier reaction time, though no participant had more than three outliers. Several BART performance outcomes (Table 4) were correlated with self-report measures of impulsivity (UPPS-P-C; Table 1). Negative urgency (the tendency to act rashly during negative emotions) and lack of premeditation (not thinking before acting) were positively correlated with number of inflations per balloon (R=0.35, p=0.01 for negative urgency and R=0.33, p=0.01 for lack of premeditation). This suggests that these traits are associated with a strategy of inflating the balloons more (i.e., maximizing reward) and not avoiding balloon explosions (i.e., avoiding punishment). In fact, negative urgency was also positively related to total earnings (R=0.39, p=0.04), suggesting that this strategy on the BART was effective. 3.3. Imaging results For main effects of task condition, with and without parametric modulator analysis, see Supplementary Fig. 1 and Supplementary Table 2. Group effects are reported below. 3.3.1. Choice conditions. No main effects of group or group × condition interactions were found for the choose contrasts (Choose Win vs. Choose Inflate). 3.3.2. Outcome conditions Parametric modulator analysis: There were main effects of group in a cluster in the bilateral ventromedial PFC (vmPFC), such that HR participants had greater activation in both conditions, as balloon explosion probability increased (for all voxels: F(1,40)>7.22,
p216 voxels; Table 5, Fig. 2). Two clusters showing a group × condition interaction spanned the ACC/ventral striatum and inferior frontal gyrus(IFG)/anterior insula (F(1,40)>7.22, p216 voxels; Table 5; Fig. 3). In these clusters, as explosion probability increased, post hoc analyses revealed that the HR participants had increasing activation and HCs decreasing activation on the Outcome Explode trials (ACC/striatum: t(40)=13.5, p=0.001; IFG/insula: t(40)=11.1, p=0.002), but no group differences on the Outcome Inflate trials. Analysis of BOLD signal, without parametric modulators: There was a group × condition interaction in the left occipital cortex (for all voxels: F(1,43)>7.22, p216 voxels; Table 6). This interaction was driven by greater activation for HR group than the HC group on the Outcome Explode, but not on the Outcome Inflate condition. 3.3.3. Functional connectivity. To further explore the findings in the striatal cluster where activity differed between groups on the outcome contrast (Fig. 3), a functional connectivity analysis was used to examine the time-series correlation between activity in this cluster and all other voxels. This analysis demonstrated that during the outcome contrast, the striatal cluster was highly functionally correlated with prefrontal and posterior (i.e., occipital) regions for both groups (see Supplemental Fig. 2). However, group differences were only found in a cluster in the thalamus ([7,-21 ,4 ], k=269; peak t =3.3 voxels, p
Neural activation during risky decisionmaking in youth at High risk for substance use Disorders Leslie A. Hulvershorn, Tom A. Hummer, Rena Fukunaga, Ellen Leibenluft, Peter Finn, Melissa A. Cyders, Amit Anand, Lauren Overhage, Allyson Dir, Joshua Brown www.elsevier.com/locate/psychresns
PII: DOI: Reference:
S0925-4927(15)00114-6 http://dx.doi.org/10.1016/j.pscychresns.2015.05.007 PSYN10368
To appear in:
Psychiatry Research: Neuroimaging
Received date: 7 July 2014 Revised date: 13 March 2015 Accepted date: 13 May 2015 Cite this article as: Leslie A. Hulvershorn, Tom A. Hummer, Rena Fukunaga, Ellen Leibenluft, Peter Finn, Melissa A. Cyders, Amit Anand, Lauren Overhage, Allyson Dir, Joshua Brown, Neural activation during risky decision-making in youth at High risk for substance use Disorders, Psychiatry Research: Neuroimaging, http://dx.doi.org/10.1016/j.pscychresns.2015.05.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Neural Activation During Risky Decision-Making in Youth at High Risk for Substance Use Disorders Leslie A. Hulvershorna*, Tom A. Hummera, Rena Fukunagab, Ellen Leibenluftc, Peter Finnb, Melissa A. Cydersd, Amit Anande, Lauren Overhagea, Allyson Dird, Joshua Brownb a
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
b
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN,
USA c
Section on Bipolar Spectrum Disorders, Intramural Research Program, NIMH,
Bethesda, MD, USA d
Department of Psychology, Indiana University Purdue University–Indianapolis,
Indianapolis, IN, USA e
Center for Behavioral Health, Cleveland Clinic, Cleveland, OH, USA
*Corresponding Author: Leslie A. Hulvershorn, MD 705 Riley Hospital Drive, Rm 4300 Indianapolis, IN 46205 Email: [email protected] Phone: 317-944-2008 Fax: 317-948-0609
Abstract Risky decision-making, particularly in the context of reward-seeking behavior, is strongly associated with the presence of substance use disorders (SUDs). However, there has been little research on the neural substrates underlying reward-related decision-making in drug-naïve youth who are at elevated risk for SUDs. Participants comprised 23 highrisk (HR) youth with a well-established SUD risk phenotype and 27 low-risk healthy comparison (HC) youth, aged 10-14. Participants completed the balloon analog risk task (BART), a task designed to examine risky decision-making, during functional magnetic resonance imaging. The HR group had faster reaction times, but otherwise showed no behavioral differences from the HC group. HR youth experienced greater activation when processing outcome, as the chances of balloon explosion increased, relative to HC youth, in ventromedial prefrontal cortex (vmPFC). As explosion probability increased, group-by-condition interactions in the ventral striatum/anterior cingulate and the anterior insula showed increasing activation in HR youth, specifically on trials when explosions occurred. Thus, atypical activation increased with increasing risk of negative outcome (i.e., balloon explosion) in a cortico-striatal network in the HR group. These findings identify candidate neurobiological markers of addiction risk in youth at high familial and phenotypic risk for SUDs. Keywords: Adolescent, Decision-making, risk, Addiction risk, Functional imaging, Prefrontal cortex
1. Introduction Decision-making refers to the process of “forming preferences, selecting and executing actions, and evaluating outcomes” (Ernst and Paulus, 2005). Theorists have identified a series of processes that occur during the choice phase of decision-making: initiation, monitoring, and completion of choice-related actions (Reyna and Rivers, 2008). The outcome phase follows, in which individuals learn and process the actual outcomes of their choices (Reyna and Rivers, 2008). Altered decision-making patterns have been observed in individuals with substance use disorders (SUDs), including preference for short-term gains (Grant et al., 2000; Bechara and Damasio, 2002) and riskier options (Lane and Cherek, 2000) and difficulty valuing the probability and magnitude of potential outcomes (Rogers and Robbins, 2001; Paulus et al., 2002; Paulus et al., 2003). Whether these decision-making deficits and their underlying neural substrates are the result of repeated use of drugs of abuse, predate SUDs, or both, remains unclear. Deficits in making choices have been hypothesized to originate from preexisting neurobiological abnormalities (Ernst and Paulus, 2005), while deficits in processing outcomes have been hypothesized to be more likely a consequence of substance use (Redish, 2004). To address this hypothesized distinction, the neural basis of decision-making must be better characterized in drug naïve individuals, with the eventual goal of longitudinally assessing neural activity in candidate regions as SUDs develop, as has been done with other imaging modalities (Norman et al., 2011). Because fewer than 15% of adolescents develop lifetime SUDs (Huang et al., 2006), targeting youth at high familial and phenotypic risk for SUDs might illuminate underlying neural mechanisms influencing the development of SUDs. Given that the mean onset of SUDs is age 14 (Swendsen et al., 2012), assessing decision-making in
high-risk preadolescent youth is warranted. Multiple addiction risk models have converged on the finding that youth with externalizing disorders [e.g., attentiondeficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD)], particularly those with a family history of addiction, are at elevated risk for the development of SUDs (Tarter et al., 2003; Zucker, 2008; Iacono et al., 2008; King et al., 2009). For example, among 10-12 year old boys followed to age 19, the risk model implemented here predicted SUDs with 85% accuracy and accounted for 50% of the variance in drug use (Tarter et al., 2003). Thus, we attempt to maximize risk for SUD development according to these models by recruiting a high-risk sample with both SUD family history and childhood externalizing psychopathology. While this complex risk phenotype does not allow for the dissociation of neural effects of externalizing psychopathology from those related to a family history of addiction, its high predictive power for SUD development is clinically significant. Findings revealed with this high-risk sample (versus healthy comparisons) warrant future investigations designed to disentangle the impact of externalizing disorders and familial factors. Most of the risky decision-making literature in externalizing disorders has focused on either adults (Miranda et al., 2009; Matthies et al., 2012; Duarte et al., 2012; Galvan et al., 2013) or youth behavioral outcomes (Drechsler et al., 2008; Fairchild et al., 2009; Drechsler et al., 2010; Schutter et al., 2011). These studies highlight an increased probability of disadvantageous decisions, correlations of risky-decision-making with impaired working memory and a propensity for lower probability/high reward choices in both youth and adults with externalizing psychopathology. Only one study appears to have directly examined the neural basis for risky-decision-making in youth with externalizing disorders (Crowley et al., 2010) and it focused on brain response during the outcome phase. Contrasting cautious low-yield with risky high-yield responses (n=20
adolescents with conduct/SUDs, in remission; n=20 adolescent controls), authors reported decreased activity during reward (anterior cingulate, temporal cortex and cerebellum) and increased activation during loss (orbitofrontal cortex, brain stem and cerebellum) in the conduct/SUD group. Thus, youth with externalizing disorders reliably demonstrate behavioral differences in risky-decision-making, but few studies have addressed the neural underpinnings of these differences. Several studies have examined neural activation associated with decisionmaking in youth identified as being at elevated risk for SUDs (Ivanov et al., 2012; Nees et al., 2012; Xiao et al., 2013). In these studies, youth deemed high risk for SUD, either by early/problem use or by family history have been shown to have activation abnormalities in cortical (OFC, insula), limbic and striatal circuits, although findings are inconsistent across studies, potentially due to heterogeneity in psychopathology and substance use. Most work has been conducted in older non-drug naïve youths, which likely confounds neural risk factors with early effects of substance use. Additionally, in this population, no studies have dissociated choice from responses to choice outcomes. Using the balloon analog risk task (BART), we examined the neural basis of choice and outcome phases of decision-making separately and characterized activation changes as both risk and reward increase across trials. The BART uses financial incentives to model real-world drug and alcohol choices by presenting participants with a series of risky-decisions and has been associated with psychopathy and impulsivity (Hunt et al., 2005), adolescent risk-taking (Lejuez et al., 2003b; Lejuez et al., 2007) and SUDs (Lejuez et al., 2003a; Hopko et al., 2006). In the sample most relevant to the present, youth with externalizing disorders (mean age 16) were found to have behavioral differences, specifically more inflations and more popped balloons, compared to healthy controls (Crowley et al., 2006). The initial version of the BART used in imaging (Rao et al., 2008) was modified from the original (Lejuez et al., 2002) to be able to dissociate
choice effects (i.e., choose to inflate or stop) from outcome effects (i.e., successful inflation, burst, and cash out). In healthy adults, fMRI BART studies have revealed that the choice phase of risky-decision making is linked to activation in a meso-limbic frontal network including midbrain, ventral and dorsal striatum, anterior insula, dorsal lateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC) (Rao et al., 2008; Fukunaga et al., 2012) and ventromedial prefrontal cortex (vmPFC) (Fukunaga et al., 2012; Schonberg et al., 2012). Findings associated with the outcome phase have been associated with a similar network involving insula, striatal, cerebellar and medial prefrontal regions (Galvan et al., 2013); Surprising outcomes have been associated with the medial PFC (Jessup et al., 2010). Increased responses to balloon explosions in lateral prefrontal cortex, insula, ACC and middle temporal gyrus was reported in adults with alcohol use disorders, relative to controls (Claus and Hutchison, 2012). Medial prefrontal activation was also correlated with adult alcohol use during balloon outcomes (Bogg et al., 2012). The BART has been studied in adolescents (Lejuez et al., 2003b; Lejuez et al., 2007), although only minimally during fMRI and not in the context of addiction risk (Chiu et al., 2012; Telzer et al., 2013b, a). We administered the BART to drug-naïve youth selected for high SUD risk (high risk; HR) and healthy comparisons (HC) during fMRI. The version of the BART used here (Bogg et al., 2012; Fukunaga et al., 2012) included a parametric modulation analysis that allowed for the study of a central question: How neural activation changes as risk for explosion changes. Since deficits in choice selection may predate drug involvement (Ernst and Paulus, 2005; Paulus et al., 2005), we hypothesize that HR youth will demonstrate insensitivity to increasing explosion probability in choice-relevant regions (e.g., anterior cingulate cortex (ACC), inferior frontal gyrus). We also hypothesize that neural response to choice outcomes will be more marked in HR youth in the vmPFC, ACC (Smith et al., 2010; Bogg et al., 2012) and
dorsal striatum, given its role in action-reward associations in humans (Balleine et al., 2007) and in prior BART studies (Rao et al., 2008). 2. Methods 2.1. Participants As detailed previously (Hulvershorn et al., 2013), we recruited right-handed, English-speaking 10-14 year-olds with at least one parent capable of reading and speaking English. To maximize familial risk for SUD development, HR participants were required to be biological offspring of men with past or present SUDs and to have an additional first- or second-degree family member with SUD history. Each HR participant also met DSM-IV-TR criteria for ADHD plus a disruptive behavior disorder [CD, ODD or disruptive behavior disorder, not otherwise specified (DBD NOS)]. More than five lifetime uses of drugs of abuse (including nicotine) or alcohol were exclusionary. HR participants were recruited largely from the community (radio, print and online ads), although a minority of youth were recruited directly from a psychiatric clinic (signs in clinic, notification by intake coordinator). HC participants had no current or lifetime history of any DSM-IV psychiatric diagnosis or SUDs (exceptions: specific phobias, enuresis, encopresis, learning disorders) and no first-degree relative with a history or current diagnosis of a SUD. HC participants were recruited in response to community postings. All individuals with in utero exposure to drugs or alcohol, per caregiver report, were excluded. Additional exclusion criteria for both groups included psychotic symptoms, pervasive developmental disorders, current depression or mania, or SUDs; psychopharmacologic treatment within the past 2 weeks other than psychostimulants (withheld the days of assessment and scanning, as is routine for pediatric ADHD neuroimaging studies, given little concern for withdrawal symptoms); history of
neurological problems; estimated Full-Scale IQ 5000 ms) was compared between groups. Balloons were also compared between groups for the following measures: balloons won, balloons exploded, inflations per balloon, minimum/maximum number of inflations and reaction times. Analysis of variance (ANOVA) was performed to examine group (HR vs. HC) by condition (choose win vs. choose inflate) interactions as well as main effects of group. To assess whether behavioral traits correlated with BART performance, UPPS-PC subscale scores were correlated with the following variables (listed in Table 4) using Pearson’s Correlations (averaged per participant): number of inflations per balloon, total number of inflations, total number of exploded, stopped and completed balloons and total average winnings.
2.6. Image analysis 2.6.1. Subject-level analyses. Image preprocessing, using AFNI software (Cox, 1996), consisted of slice-time correction, de-spiking of time series outliers (3dDespike algorithm), motion correction via realignment to the first time point using Fourier interpolation, registering the functional image to the structural image, correction for signal inhomogeneity with field mapping and spatial smoothing with a Gaussian kernel of 6-mm full-width at half-maximum. A general linear regression model (GLM) with random effects was created to estimate event-related responses. Along with six motion parameters and linear and quadratic detrending terms to correct for potential scanner drift, nine regressors that encompassed all potential decisions and outcomes, including parametric modulators when appropriate, and one additional nuisance regressor (reaction time outliers) were generated. Time points with >3.5-mm head displacement were censored (plus the time point before and two after). Participants with >10 time points censored were excluded. Choice events, aligned to the one repetition time (TR) that included the button press response, were modeled as Choose Inflate (choosing to continue inflating the balloon) or Choose Win (choosing to discontinue inflating and bank money) regressors. Outcome events were modeled as the TR that included balloon explosion (Outcome Explode), successful balloon inflation (Outcome Inflate), or the outcome of choosing to discontinue inflations (Outcome Win). Because there were relatively few Outcome Explode events, outliers (1.5*interquartile range) among those participants with fewer than five balloon explosions were excluded from Outcome contrasts (n=3 participants). For each subject, Choose Inflate and Choose Win conditions were contrasted. For outcome trials, only Outcome Inflate vs. Outcome Explode were contrasted, as these constitute the outcomes (positive or negative) that could follow an identical earlier
decision (Choose Inflate). We did not contrast Outcome Win (vs. either Outcome Inflate or Explode) because there was no uncertainty at this point in the trial (i.e., the subject had already banked the reward). Balloon explosion probabilities (Supplementary Table 1) were included as parametric modulators for each event-type regressor (e.g., Choose Inflate * P(explode), Outcome Inflate * P(explode)), except for Outcome Win, because the probability of explosion no longer applied at this point. The parametric modulator for a given inflation represented the explosion probability at each pump, not the total explosion probability that occurs until the balloon either explodes or is cashed outInclusion of parametric modulators accounts, in part, for the psychological impact of the changing balloon explosion probability across trials. Trials with reaction time outliers (>5000 ms) were modeled separately, but identically to all other balloons, and treated as nuisance regressors. Activation maps were warped to a standard Talairach atlas for group analyses. For subjects where P(explode) was the same for each explosion (2 HR, 1 HC), modulatory effects could not be separated from effects of the explosion itself, so these participants were excluded from outcome contrasts. 2.6.2. Group level analyses. Contrast maps for choice (Choose Inflate vs. Choose Win) and outcome (Outcome Inflate vs. Outcome Explode) were obtained separately from subject-level coefficients. To test for between-group differences on the contrasts, we carried out condition (e.g., Choose Inflate vs. Choose Win) by group within-subjects ANOVAs (using 3dMVM in AFNI) on activation intensities derived from the GLM (“Analysis of BOLD signal, without Parametric Modulation”). In addition, choice and outcome ANOVAs with parametric modulators (P(explode)) were also conducted
(“Parametric Modulator Analysis”). Activation intensities from each subject were extracted from significant clusters. Separately, to examine the potential influence of IQ or socioeconomic status (SES) on the findings, activation intensities from significant clusters were tested with analysis of covariance (ANCOVA) using SES (defined using family income on a 1-5 scale, where 1 = $80,000; Table 1) and full-scale IQ as covariates. Only clusters which remained significant (p < 0.05) after accounting for covariates are reported as primary findings. Incidentally, all clusters remained significant after accounting for covariates. Multiple comparisons associated with this whole-brain voxel-wise analysis were addressed using cluster-wise thresholds. Individual voxels were considered significant at p < 0.01, and a Monte Carlo simulation (AlphaSim) was again used to determine that cluster size (k) > 216 voxels corrected for group-level significance (p < 0.05). 3. Results 3.1. Participants Fifty-six right-handed male and female participants aged 10-14 years old completed the protocol. Six participants were excluded from all analyses for the following reasons: (1) two HR participants had >10 motion-censored time points; (2) two HR participants had high global signal variance across the time series, likely non-neural artifact; and (3) two HC participants had >10 trials with reaction times exceeding 5000 ms. Groups did not differ on motion in the scanner (Table 1). Groups were matched on age, gender, and Tanner stage, but they differed on IQ and SES (Table 1). Psychotropic medication treatment histories are presented in Table
1. See Table 2 for clinical characteristics of the child participants and Table 3 for paternal SUDs. 3.2. BART performance Groups did not differ on task outcomes, except for reaction times (Table 4), where HR youth were faster on win trials (F(1, 46)=7.27, p=0.01). Twelve participants (11 HC; 1 HR) required modeling with the nuisance regressor for outlier reaction time, though no participant had more than three outliers. Several BART performance outcomes (Table 4) were correlated with self-report measures of impulsivity (UPPS-P-C; Table 1). Negative urgency (the tendency to act rashly during negative emotions) and lack of premeditation (not thinking before acting) were positively correlated with number of inflations per balloon (R=0.35, p=0.01 for negative urgency and R=0.33, p=0.01 for lack of premeditation). This suggests that these traits are associated with a strategy of inflating the balloons more (i.e., maximizing reward) and not avoiding balloon explosions (i.e., avoiding punishment). In fact, negative urgency was also positively related to total earnings (R=0.39, p=0.04), suggesting that this strategy on the BART was effective. 3.3. Imaging results For main effects of task condition, with and without parametric modulator analysis, see Supplementary Fig. 1 and Supplementary Table 2. Group effects are reported below. 3.3.1. Choice conditions. No main effects of group or group × condition interactions were found for the choose contrasts (Choose Win vs. Choose Inflate). 3.3.2. Outcome conditions Parametric modulator analysis: There were main effects of group in a cluster in the bilateral ventromedial PFC (vmPFC), such that HR participants had greater activation in both conditions, as balloon explosion probability increased (for all voxels: F(1,40)>7.22,
p216 voxels; Table 5, Fig. 2). Two clusters showing a group × condition interaction spanned the ACC/ventral striatum and inferior frontal gyrus(IFG)/anterior insula (F(1,40)>7.22, p216 voxels; Table 5; Fig. 3). In these clusters, as explosion probability increased, post hoc analyses revealed that the HR participants had increasing activation and HCs decreasing activation on the Outcome Explode trials (ACC/striatum: t(40)=13.5, p=0.001; IFG/insula: t(40)=11.1, p=0.002), but no group differences on the Outcome Inflate trials. Analysis of BOLD signal, without parametric modulators: There was a group × condition interaction in the left occipital cortex (for all voxels: F(1,43)>7.22, p216 voxels; Table 6). This interaction was driven by greater activation for HR group than the HC group on the Outcome Explode, but not on the Outcome Inflate condition. 3.3.3. Functional connectivity. To further explore the findings in the striatal cluster where activity differed between groups on the outcome contrast (Fig. 3), a functional connectivity analysis was used to examine the time-series correlation between activity in this cluster and all other voxels. This analysis demonstrated that during the outcome contrast, the striatal cluster was highly functionally correlated with prefrontal and posterior (i.e., occipital) regions for both groups (see Supplemental Fig. 2). However, group differences were only found in a cluster in the thalamus ([7,-21 ,4 ], k=269; peak t =3.3 voxels, p
